Storage, retrieval, and analysis of large data sets has become increasingly complex as the quantity and need for such data has increased dramatically over the years. “Big data” is a term that is now utilized to describe data sets that have such high levels of complexity and/or breadth that traditional data processing applications are not adequate to manage and maintain the data. Accordingly, to meet the needs associated with “big data”, much Information Technology (IT) investment is often required to purchase or develop specialized data management hardware and software solutions. Two of the most commonly utilized proprietary solutions comprise data storage systems and proprietary software applications offered by the Oracle® Corporation of Redwood City, Calif. (e.g., Oracle® Database 12c™) and Teradata® of Dayton, Ohio (e.g., the Teradata® Integrated Big Data Platform). The cost of such proprietary end-to-end big data solutions is significant, however, including the necessity of end-users being properly trained for each implemented proprietary platform. In an effort to reduce the proprietary known-how overhead of big data systems, open-source solutions such as Hadoop® available from the Apache™ Software Foundation of Forest Hill, Md., have been developed. Hadoop® leverages a “MapReduce” programming architecture (as opposed to the traditional Relational Database Management System (RDBMS) model) and utilizes distributed storage and data processing to manage large data sets. For data transfer operations, Apache™ Sqoop™ (SQL+Hadoop®) is available to handle bulk data migration between the Hadoop® Distributed File System (HDFS) and relational databases, utilizing a command-line interface. Even with these open-source tools, however, the cost of end-user training remains high and data transfer procedures remain limited to specialized personnel.